code: def test_fabric_optimizer_load_state_dict():
      """Test that the FabricOptimizer can load the state dict on the wrapped optimizer and update its internal
      `__dict__`."""
      model = torch.nn.Linear(1, 1)
      optimizer = torch.optim.Adam(model.parameters())
      assert not optimizer.state  # a fresh optimizer has no state
      model(torch.rand(1)).backward()
      optimizer.step()
      assert optimizer.state
      state_dict = optimizer.state_dict()
  
      optimizer = torch.optim.Adam(
          model.parameters()
      )  # fresh optimizer
      fabric_optimizer = _FabricOptimizer(
          optimizer=optimizer, strategy=Mock()
      )
      assert (
          not fabric_optimizer.state
      )  # a fresh optimizer has no state
      fabric_optimizer.load_state_dict(state_dict)
      assert fabric_optimizer.state
      assert (
          fabric_optimizer.optimizer.state_dict()["state"]
          == state_dict["state"]
      )
  
imports: from unittest import mock
  from unittest.mock import Mock, call
  import pytest
  import torch
  from lightning.fabric.fabric import Fabric
  from lightning.fabric.plugins import Precision
  from lightning.fabric.utilities.device_dtype_mixin import _DeviceDtypeModuleMixin
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
  from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer, _unwrap_objects, is_wrapped
  from torch.utils.data import BatchSampler, DistributedSampler
  from torch.utils.data.dataloader import DataLoader
  from tests_fabric.helpers.runif import RunIf
label_desc: Use image node_id 13 for calling a global function with example usage: test_fabric_optimizer_load_state_dict() without return types